text: """
  Usage:
  
  # Create train data:
  python generate_tfrecord.py --label=<LABEL> --csv_input=<PATH_TO_ANNOTATIONS_FOLDER>/train_labels.csv  --output_path=<PATH_TO_ANNOTATIONS_FOLDER>/train.record <PATH_TO_ANNOTATIONS_FOLDER>/label_map.pbtxt
  
  # Create test data:
  python generate_tfrecord.py --label=<LABEL> --csv_input=<PATH_TO_ANNOTATIONS_FOLDER>/test_labels.csv  --output_path=<PATH_TO_ANNOTATIONS_FOLDER>/test.record  --label_map <PATH_TO_ANNOTATIONS_FOLDER>/label_map.pbtxt
  """
  
  from __future__ import division
  from __future__ import print_function
  from __future__ import absolute_import
  
  import os
  import io
  import pandas as pd
  import tensorflow as tf
  import sys
  
  sys.path.append("../../models/research")
  
  from PIL import Image
  from object_detection.utils import dataset_util
  from collections import namedtuple, OrderedDict
  
  flags = tf.app.flags
  flags.DEFINE_string("csv_input", "", "Path to the CSV input")
  flags.DEFINE_string("output_path", "", "Path to output TFRecord")
  flags.DEFINE_string(
      "label_map",
      "",
      "Path to the `label_map.pbtxt` contains the <class_name>:<class_index> pairs generated by `xml_to_csv.py` or manually.",
  )
  # if your image has more labels input them as
  # flags.DEFINE_string('label0', '', 'Name of class[0] label')
  # flags.DEFINE_string('label1', '', 'Name of class[1] label')
  # and so on.
  flags.DEFINE_string("img_path", "", "Path to images")
  FLAGS = flags.FLAGS
  
  
  def split(df, group):
      data = namedtuple("data", ["filename", "object"])
      gb = df.groupby(group)
      return [
          data(filename, gb.get_group(x))
          for filename, x in zip(gb.groups.keys(), gb.groups)
      ]
  
  
  def create_tf_example(group, path, label_map):
      with tf.gfile.GFile(os.path.join(path, "{}".format(group.filename)), "rb") as fid:
          encoded_jpg = fid.read()
      encoded_jpg_io = io.BytesIO(encoded_jpg)
      image = Image.open(encoded_jpg_io)
      width, height = image.size
  
      filename = group.filename.encode("utf8")
      image_format = b"jpg"
      # check if the image format is matching with your images.
      xmins = []
      xmaxs = []
      ymins = []
      ymaxs = []
      classes_text = []
      classes = []
  
      for index, row in group.object.iterrows():
          xmins.append(row["xmin"] / width)
          xmaxs.append(row["xmax"] / width)
          ymins.append(row["ymin"] / height)
          ymaxs.append(row["ymax"] / height)
          row["class"] = str(row["class"])
          classes_text.append(row["class"].encode("utf8"))
          class_index = label_map.get(row["class"])
          assert (
              class_index is not None
          ), "class label: `{}` not found in label_map: {}".format(
              row["class"], label_map
          )
          classes.append(class_index)
  
      tf_example = tf.train.Example(
          features=tf.train.Features(
              feature={
                  "image/height": dataset_util.int64_feature(height),
                  "image/width": dataset_util.int64_feature(width),
                  "image/filename": dataset_util.bytes_feature(filename),
                  "image/source_id": dataset_util.bytes_feature(filename),
                  "image/encoded": dataset_util.bytes_feature(encoded_jpg),
                  "image/format": dataset_util.bytes_feature(image_format),
                  "image/object/bbox/xmin": dataset_util.float_list_feature(xmins),
                  "image/object/bbox/xmax": dataset_util.float_list_feature(xmaxs),
                  "image/object/bbox/ymin": dataset_util.float_list_feature(ymins),
                  "image/object/bbox/ymax": dataset_util.float_list_feature(ymaxs),
                  "image/object/class/text": dataset_util.bytes_list_feature(
                      classes_text
                  ),
                  "image/object/class/label": dataset_util.int64_list_feature(classes),
              }
          )
      )
      return tf_example
  
  
  def main(_):
      writer = tf.python_io.TFRecordWriter(FLAGS.output_path)
      path = os.path.join(os.getcwd(), FLAGS.img_path)
      examples = pd.read_csv(FLAGS.csv_input)
  
      # Load the `label_map` from pbtxt file.
      from object_detection.utils import label_map_util
  
      label_map = label_map_util.load_labelmap(FLAGS.label_map)
      categories = label_map_util.convert_label_map_to_categories(
          label_map, max_num_classes=90, use_display_name=True
      )
      category_index = label_map_util.create_category_index(categories)
      label_map = {}
      for k, v in category_index.items():
          label_map[v.get("name")] = v.get("id")
  
      grouped = split(examples, "filename")
      for group in grouped:
          tf_example = create_tf_example(group, path, label_map)
          writer.write(tf_example.SerializeToString())
  
      writer.close()
      output_path = os.path.join(os.getcwd(), FLAGS.output_path)
      print("Successfully created the TFRecords: {}".format(output_path))
  
  
  if __name__ == "__main__":
      tf.app.run()
  